Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

Biology (Basel). 2020 Dec 18;9(12):477. doi: 10.3390/biology9120477.

Abstract

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.

Keywords: Covid-19; Covid-19 spread predicting; SARS-CoV-2; curve fitting; epidemic dynamics; epidemic-fitted wavelet; model selection.